Risk factors of severe influenza A H1N1: a Meta-analysis
Bibliographic record
Abstract
Objective To explore the risk factors of severe influenza A H1N1 by the Meta-analysis so as to provide a reference for prevention and control. Methods Literatures on risk factors of severe influenza A H1N1 were retrieved in Chinese Biomedical Medicine, China national knowledge internet (CNKI), Wanfang Database, VIP Database, PubMed and ProQuest from 1st January 2000 to 31st December 2018 by computer. Quality of literatures were evaluated with the Newcastle-Ottawa Scale (NOS) on standard quality evaluation. Heterogeneity test of literatures was analyzed with the RevMan 5.1, and the odds ratio (OR) and 95% confidence interval (CI) was calculated with the Meta-analysis. Results A total of 10 literatures were included. Among those literatures, there were 1 852 cases in case group, 3 049 cases in control group and 8 risk factors. Meta-analysis showed that the risk factors of severe influenza A H1N1 included ages ≤5 years (OR=6.09, 95%CI: 1.77-21.16) , pregnancy (OR=11.80, 95%CI: 6.91-20.16) , chronic underlying disease (OR=3.74, 95%CI: 2.34-5.97) , BMI≥30 kg/m2 (OR=4.48, 95%CI: 2.81-7.12) , time between attack and seeking medical advice≥48 h (OR=1.85, 95%CI: 1.50-2.28) and infected with HIV (OR=1.74, 95%CI: 1.33-2.27) with statistical differences (P<0.001) . Influenza vaccination which was a protective factor had a negative influence on incidence of severe influenza A H1N1 (OR=0.63, 95%CI: 0.49-0.82) . Conclusions Risk factors of severe influenza A H1N1 comprise ages≤5 years, pregnancy, chronic underlying disease, BMI≥30 kg/m2, time between attack and seeking medical advice≥48 h and infected with HIV, and influenza vaccination is a protective factor. Influenza vaccination can effectively reduce the incidence of severe influenza A H1N1 or slow down the disease progression. Evidence on gender as a risk factor of severe influenza A H1N1 is insufficient which needs to be tested by later researches. Key words: Influenza A virus, H1N1 subtype; Risk factors; Meta-analysis
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.063 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".